168 research outputs found

    The posterity of Zadeh's 50-year-old paper: A retrospective in 101 Easy Pieces – and a Few More

    Get PDF
    International audienceThis article was commissioned by the 22nd IEEE International Conference of Fuzzy Systems (FUZZ-IEEE) to celebrate the 50th Anniversary of Lotfi Zadeh's seminal 1965 paper on fuzzy sets. In addition to Lotfi's original paper, this note itemizes 100 citations of books and papers deemed “important (significant, seminal, etc.)” by 20 of the 21 living IEEE CIS Fuzzy Systems pioneers. Each of the 20 contributors supplied 5 citations, and Lotfi's paper makes the overall list a tidy 101, as in “Fuzzy Sets 101”. This note is not a survey in any real sense of the word, but the contributors did offer short remarks to indicate the reason for inclusion (e.g., historical, topical, seminal, etc.) of each citation. Citation statistics are easy to find and notoriously erroneous, so we refrain from reporting them - almost. The exception is that according to Google scholar on April 9, 2015, Lotfi's 1965 paper has been cited 55,479 times

    Uncertain non near system control with Fuzzy Differential Equations and Z-numbers

    Get PDF
    In this paper, the solutions of fuzzy differential equations (FDEs) are estimated by using two types of Bernstein neural networks. Here, the uncertainties are in the form of Z numbers. Firstly, we transform the FDE to four ordinary differential equations (ODEs) at par with Hukuhara differentiability. After that we develop neural models having the structure of ODEs. By using modified backpropagation technique for Z number variables, the training of neural networks are carried out. The results of the simulation illustrate that these innovative models, Bernstein neural networks, are efficient to approximate the solutions of FDEs which are on the basis of Z-numbers

    Reasoning under fuzzy vagueness and probabilistic uncertainty in the Semantic Web

    Get PDF
    Combining data from many different sources or from sources that are not entirely trusted brings challenges to the automated processing of such data. Knowledge presented in natural language is another challenge for computing. In the semantic web, many applications such as personal agents need to be able to manage multiple kinds of uncertainty. There are two main approaches to modeling uncertainty in the literature - fuzzy and probabilistic. These approaches model semantically different types of uncertainty. This paper focuses on approaches that combine both fuzzy and probabilistic reasoning in one framework to provide automated agents the capability to deal with both types of uncertainty

    Discontinuous rock slope stability analysis under blocky structural sliding by fuzzy key-block analysis method

    Get PDF
    This study presents a fuzzy logical decision-making algorithm based on block theory to effectively determine discontinuous rock slope reliability under various wedge and planar slip scenarios. The algorithm was developed to provide rapid response operations without the need for extensive quantitative stability evaluations based on the rock slope sustainability ratio. The fuzzy key-block analysis method utilises a weighted rational decision (multi-criteria decision-making) function to prepare the 'degree of reliability (degree of stability-instability contingency)' for slopes as implemented through the Mathematica software package. The central and analyst core of the proposed algorithm is provided as based on discontinuity network geometrical uncertainties and hierarchical decision-making. This algorithm uses block theory principles to proceed to rock block classification, movable blocks and key-block identifications under ambiguous terms which investigates the sustainability ratio with accurate, quick and appropriate decisions especially for novice engineers in the context of discontinuous rock slope stability analysis. The method with very high precision and speed has particular matches with the existing procedures and has the potential to be utilised as a continuous decision-making system for discrete parameters and to minimise the need to apply common practises. In order to justify the algorithm, a number of discontinuous rock mass slopes were considered as examples. In addition, the SWedge, RocPlane softwares and expert assignments (25-member specialist team) were utilised for verification of the applied algorithm which led to a conclusion that the algorithm was successful in providing rational decision-making

    Numerical Solution of Fuzzy Equations with Z-numbers using Neural Networks

    Get PDF
    In this paper, the uncertainty property is represented by the Z-number as the coefficients of the fuzzy equation. This modification for the fuzzy equation is suitable for nonlinear system modeling with uncertain parameters. We also extend the fuzzy equation into dual type, which is natural for linear-in-parameter nonlinear systems. The solutions of these fuzzy equations are the controllers when the desired references are regarded as the outputs. The existence conditions of the solutions (controllability) are proposed. Two types of neural networks are implemented to approximate solutions of the fuzzy equations with Z-number coefficients

    Group-decision making with induced ordered weighted logarithmic aggregation operators

    Get PDF
    This paper presents the induced generalized ordered weighted logarithmic aggregation (IGOWLA) operator, this operator is an extension of the generalized ordered weighted logarithmic aggregation (GOWLA) operator. It uses order-induced variables that modify the reordering process of the arguments included in the aggregation. The principal advantage of the introduced induced mechanism is the consideration of highly complex attitude from the decision makers. We study some families of the IGOWLA operator as measures for the characterization of the weighting vector (...

    The legacy of 50 years of fuzzy sets: A discussion

    Get PDF
    International audienceThis note provides a brief overview of the main ideas and notions underlying fifty years of research in fuzzy set and possibility theory, two important settings introduced by L.A. Zadeh for representing sets with unsharp boundaries and uncertainty induced by granules of information expressed with words. The discussion is organized on the basis of three potential understanding of the grades of membership to a fuzzy set, depending on what the fuzzy set intends to represent: a group of elements with borderline members, a plausibility distribution, or a preference profile. It also questions the motivations for some existing generalized fuzzy sets. This note clearly reflects the shared personal views of its authors

    Fuzzy Reinforcement Learning using Neural Network: An Application to Medical Diagnosis and Business Intelligence

    Get PDF
    The information available to the system is incomplete in many applications particularly in Decision Support Systems The fuzzy logic deals incomplete information with belief rather than likelihood probability Sometimes the decision has to be taken with fuzzy information In this paper fuzzy machine learning is studied for decision support systems The fuzzy Decision set is defined with two-fold fuzzy set The fuzzy inference is studied with fuzzy neural network for fuzzy Decision sets Business application is given as applicatio
    • …
    corecore